A Study on the Effect of Workplace Friendship Level on Consultant's Customer Orientation - Focusing on the Mediating Effect of Job Commitment
Bibliographic record
Abstract
Background/Objectives: The purpose of this study was to examine the effect of the relationship between human resources and members on the consultant's customer service orientation as a way to secure a competitive advantage in order to continuously grow and develop a consulting firm.Methods/Statistical analysis: This study surveyed the consultants of consulting firms. The total number of samples used in the study was 180. The survey items consisted of 33 questions and the Likert 5-point scale was used for the measurement. As an empirical analysis, SPSS 22.0 was used for frequency analysis, exploratory factor analysis, reliability analysis, correlation analysis, regression analysis, and mediation effect analysis.Findings: According to the results of this study, the results were summarized as follows. First, Supervisor's friendship will affect the consultant's Job commitment. Second. Co-worker's friendship will affect the consultant's Job commitment. Third, Subordinate's friendship will affect the consultant's Job commitment. Fourth Supervisor's friendship will affect the consultant's customer orientation. Fifth, Co-worker's friendship will affect the consultant's customer orientation. Sixth, Subordinate's friendship will affect the consultant's customer orientation. In addition, there is no direct influence on customer orientation in the mediation of job commitment between friendship with supervisors, friends with co-workers, friends with subordinates, and consultants' customer orientation. Job involvement between friends, friends with subordinates, and consultants' customer orientation has been found to play a full mediating role as an indirect effect.Improvements/Applications: As a means of securing sustainable competitiveness, the relationship between the level of friendship in the workplace and the customer orientation of consultants was identified. Therefore, by measuring the level of friendships for the bosses, colleagues, and subordinates in various ways, the group that had the most positive influence was identified, and the opportunity to make practical contributions was prepared. However, in the future, it is required to consider the contract type of the members of the organization, the project status of each team, and the compensation system as a control variable.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".